Type I Interferon Pathway-Related Hub Genes as a Potential Therapeutic Target for SARS-CoV-2 Omicron Variant-Induced Symptoms

Background: The global pandemic of COVID-19 is caused by the rapidly evolving severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The clinical presentation of SARS-CoV-2 Omicron variant infection varies from asymptomatic to severe disease with diverse symptoms. However, the underlying mechanisms responsible for these symptoms remain incompletely understood. Methods: Transcriptome datasets from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients infected with the Omicron variant and healthy volunteers were obtained from public databases. A comprehensive bioinformatics analysis was performed to identify hub genes associated with the Omicron variant. Hub genes were validated using quantitative RT-qPCR and clinical data. DSigDB database predicted potential therapeutic agents. Results: Seven hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) were potential biomarkers for Omicron infection’s symptomatic diagnosis and treatment. Type I interferon-related hub genes regulated Omicron-induced symptoms, which is supported by independent datasets and RT-qPCR validation. Immune cell analysis showed elevated monocytes and reduced lymphocytes in COVID-19 patients, which is consistent with retrospective clinical data. Additionally, ten potential therapeutic agents were screened for COVID-19 treatment, targeting the hub genes. Conclusions: This study provides insights into the mechanisms underlying type I interferon-related pathways in the development and recovery of COVID-19 symptoms during Omicron infection. Seven hub genes were identified as promising biological biomarkers for diagnosing and treating Omicron infection. The identified biomarkers and potential therapeutic agent offer valuable implications for Omicron’s clinical manifestations and treatment strategies.


Introduction
The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has presented an unparalleled global public health challenge. SARS-CoV-2 infection leads to a spectrum of clinical outcomes, ranging from asymptomatic cases to severe illness characterized by symptoms such as high fever, cough, fatigue, and dyspnea, ultimately leading to respiratory failure [1,2]. Since 2019, there have been more than 755 million cumulative cases of COVID-19 globally and more than 6.83 million deaths [3]. No specific antiviral therapy for the pandemic COVID-19 exists yet, particularly for the milder-seeming Omicron variant. Numerous vaccines are under development, and several previously FDA-approved drugs have been repurposed to slow the progression of COVID-19 [4]. cation of hub genes. To validate the diagnostic efficacy of the hub genes, the GSE179627 dataset was used as an independent validation set. Furthermore, the GSE167930 dataset, comprising 21 healthy controls, 7 asymptomatic infected patients, 13 symptomatic infected patients, and 15 recovering patients, was utilized to identify central genes associated with COVID-19 symptoms. A schematic representation of the study design can be found in Figure 1.
dataset was used as an independent validation set. Furthermore, the GSE167930 dataset, comprising 21 healthy controls, 7 asymptomatic infected patients, 13 symptomatic infected patients, and 15 recovering patients, was utilized to identify central genes associated with COVID-19 symptoms. A schematic representation of the study design can be found in Figure 1.
To ensure the quality of the dataset samples, we utilized the R package "arrayQuali-tyMetrics". Data standardization was performed using the "affy" or "limma" packages, renowned for their application in linear models for microarray data. The identification of statistically significant differentially expressed genes (DEGs) between COVID-19 and control samples in each dataset was performed using the "limma (version 3.40.6)" package in R. DEGs with adjusted p-values < 0.05 and |log2 Fold change (logFC)| > 2 were considered statistically significant. To visualize the results, volcanic and thermal maps were generated using the R packages "ggplot2 (version 3.3.6)" and "heatmap", respectively.

Functional Enrichment Analysis
Gene ontology (GO) is widely used for gene annotation, including molecular functions (MF), biological pathways (BP), and cellular components (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis provides valuable insights into gene functions and high-level genomic functional information. To gain a comprehensive understanding of the role of target genes, we utilized the "clusterProfiler (version 4.4.4)" package in R for analyzing GO functions and performing KEGG pathway enrichment analysis. The results of the enrichment analysis were visualized using the "ggplot2 (version 3.3.6)" package. Visualizations such as string and bubble plots were generated to To ensure the quality of the dataset samples, we utilized the R package "arrayQuali-tyMetrics". Data standardization was performed using the "affy" or "limma" packages, renowned for their application in linear models for microarray data. The identification of statistically significant differentially expressed genes (DEGs) between COVID-19 and control samples in each dataset was performed using the "limma (version 3.40.6)" package in R. DEGs with adjusted p-values < 0.05 and |log2 Fold change (logFC)| > 2 were considered statistically significant. To visualize the results, volcanic and thermal maps were generated using the R packages "ggplot2 (version 3.3.6)" and "heatmap", respectively.

Functional Enrichment Analysis
Gene ontology (GO) is widely used for gene annotation, including molecular functions (MF), biological pathways (BP), and cellular components (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis provides valuable insights into gene functions and high-level genomic functional information. To gain a comprehensive understanding of the role of target genes, we utilized the "clusterProfiler (version 4.4.4)" package in R for analyzing GO functions and performing KEGG pathway enrichment analysis. The results of the enrichment analysis were visualized using the "ggplot2 (version 3.3.6)" package. Visualizations such as string and bubble plots were generated to depict the joint logFC enrichment analysis of GO and KEGG, utilizing the "ggplot2 (version 3.3.6)" package.

Protein-Protein Interaction (PPI) Analysis of Differentially Expressed Genes and Identification of Hub Genes
The protein-protein interaction (PPI) network of differentially expressed genes was examined using the STRING database (accessible at https://string-db.org/, accessed on 5 March 2023). Hub genes, which exert a significant influence on other genes within the network, were identified based on their centrality scores. The analysis data from STRING were imported into Cytoscape software (version 3.8.1). We designated the top 10 scoring genes as hub genes using the MCC algorithm from Cytoscape's cytoHubba plugin. Additionally, the co-expression network of DEGs was analyzed using Cytoscape's MCODE plugin, and the most significant clusters containing hub genes were visualized. Further analysis involved a Venn diagram to determine the intersection of hub genes obtained from these two methods, resulting in the final set of identified hub genes.

Construction of the miRNA-Target Regulatory Network
To predict the miRNAs and TFs associated with the hub genes, we utilized the miRNet database (accessible at https://www.miRNet.ca/, accessed on 22 Apirl 2023). Subsequently, we constructed and visualized regulatory networks involving mRNA-miRNA and mRNA-TF interactions using Cytoscape software (version 3.8.1).

Validation of the Diagnostic Value of Hub Genes
To assess the sensitivity and specificity of the target genes, we conducted ROC curve analysis using HiPlot software (version 0.1.0). Multigene ROC analysis was performed by calculating the predicted probability of multiple genes contributing to the results in each sample based on a binary Logit model. This analysis was conducted using SPSS 25.0 software. The results were quantified as the area under the ROC curve (AUC), considering genes with an AUC > 0.6 as diagnostically significant.

Immune Infiltration Analysis
The proportion of 22 immune cell types in the samples was estimated using "CIBER-SORTx" (accessible at https://cibersortx.stanford.edu/, accessed on 20 May 2023). CIBER-SORTx is an analytical tool that utilizes gene expression data to provide mixed estimates of the abundance of different immune cell types within a cell population.

Retrospective Analysis of Blood Counts in COVID-19 Patients
Demographic and clinical information of COVID-19 patients, including gender, age, and symptoms, were collected using an electronic case system. Each isolate was cultured and verified. Continuous variables were presented as median (interquartile range [IQR]). Differences in continuous variables between two groups were assessed using the Mann-Whitney Wilcoxon rank-sum test. A p-value < 0.05 was considered statistically significant.

Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) Verification
To validate the findings obtained from the bioinformatics analysis, peripheral blood mononuclear cell (PBMC) samples were collected from 20 COVID-19 patients infected with the Omicron variant and 20 healthy individuals as controls. Total RNA was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), and approximately 2 µg of total RNA was reverse transcribed using the iScript cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). RT-qPCR was performed on a CFX Connect Real-Time PCR detection system (Bio-Rad) using ChamQ SYBR Color qPCR Master Mix (Vazyme, Nanjing, China). The relative mRNA expression was calculated using the 2 −∆∆Ct method. The primer sequences used in the experiment are provided in Table 1. Statistical analysis was performed using one-way analysis of variance with SPSS 25.0 software (IBM, Armonk, NY, USA), and statistical significance was defined as a p-value < 0.05.

Prediction of Potential Therapeutic Agents
The DSigDB database (available at http://tanlab.ucdenver.edu/DSigDB, accessed on 30 May 2023) was utilized to predict potential therapeutic agents for COVID-19 based on protein-drug interaction data. The thresholds set for selection were FDR < 0.05 and composite score > 5000.

Statistical Analysis
Statistical analyses were performed using GraphPad Prism 9 and R software (version 4.2.2). The data were presented as mean ± standard deviation, and a comparison between groups was conducted using an unpaired Student's t-test. A p-value less than 0.05 was considered statistically significant.

Screening and Functional Enrichment Analysis of Differentially Expressed Genes in PBMC of Omicron Infection
The differential gene analysis of the GSE201530 dataset was conducted using the "limma" package in R, resulting in the identification of 73 differentially expressed genes (33 up-regulated and 40 down-regulated), as shown in Figure 2A (Supplementary Table S1). The screening criteria applied were |log2(FC)| > 2 and adj. p-value < 0.05.
To evaluate the reproducibility of the data within the group, UMAP analysis was performed, demonstrating satisfactory reproducibility, as depicted in Figure 2B. Volcano plots illustrating the differentially expressed genes were generated using the "ggplot2 [3.3.6]" package in R, with the parameters Log2FC > 2 and adj. p-value < 0.05, as shown in Figure 2C.
GO and KEGG enrichment analyses were conducted on the differentially expressed genes (Supplementary Table S2). The results revealed significant GO enrichments related to virus response, defense response to symbiont, defense response to virus, response to type I interferon, regulation of viral life cycle, cellular response to type I interferon, and the type I interferon signaling pathway. In the KEGG enrichment analysis, the differentially expressed genes were primarily associated with diseases such as COVID-19, Influenza A, and Chagas disease, as depicted in Figure 2E-H. differentially expressed genes were primarily associated with diseases such as COVID Influenza A, and Chagas disease, as depicted in Figure 2E-H.

Gene Screening and Functional Enrichment Analysis of PBMC Hub Genes in Omicron Infection
The differentially expressed genes obtained earlier were used to construct a prot protein interaction network using the STRING database (accessible at https://str db.org/, accessed on 5 March 2023) ( Figure 3A). The data from STRING were impor
Following the hub genes analysis, GO and KEGG enrichment analyses were performed (Supplementary Table S3). The results demonstrated significant enrichment in GO terms associated with the response to virus, response to type I interferon, cellular response to type I interferon, type I interferon signaling pathway, and regulation of type I interferon-mediated signaling. The KEGG analysis revealed the involvement of multiple viral infectious diseases, including COVID-19 ( Figure 3F-I).

Confirmation of Hub Genes Expression and Diagnostic Value in GSE179627
GSE179627 was utilized to verify the expression levels of the selected target genes. The results demonstrated consistent expression patterns between COVID-19 patients with Omicron infection and healthy individuals for the 10 hub genes (OAS1, IFI44, IFI44L, MX1, OAS3, USP18, IFIT1, RSAD2, IFI27, and ISG15) ( Figure 4A-J).
ROC curves were generated using the data from COVID-19 patients with Omicron infection and healthy individuals to assess the diagnostic value of these 10 genes. The results indicated that these genes hold significant diagnostic value for COVID-19 patients.
The AUC values were as follows: OAS1, 0.8352 (95% CI: 0.6697 to 1.000); IFI44, 0.8409 (95% CI: 0.6974 to 0.9844); IFI44L, 0.8561 (95% CI: 0.7306 to 0.9815); MX1, 0.9091 (95% CI: 0.8048 Following the hub genes analysis, GO and KEGG enrichment analyses were performed (Supplementary Table S3). The results demonstrated significant enrichment in GO terms associated with the response to virus, response to type I interferon, cellular response to type I interferon, type I interferon signaling pathway, and regulation of type I interferon-mediated signaling. The KEGG analysis revealed the involvement of multiple viral infectious diseases, including COVID-19 ( Figure 3F-I).

Confirmation of Hub Genes Expression and Diagnostic Value in GSE179627
GSE179627 was utilized to verify the expression levels of the selected target genes. The results demonstrated consistent expression patterns between COVID-19 patients with Omicron infection and healthy individuals for the 10 hub genes (OAS1, IFI44, IFI44L, MX1, OAS3, USP18, IFIT1, RSAD2, IFI27, and ISG15) ( Figure 4A-J).
ROC curves were generated using the data from COVID-19 patients with Omicron infection and healthy individuals to assess the diagnostic value of these 10 genes. The results indicated that these genes hold significant diagnostic value for COVID- 19

Investigation of the Relationship between Hub Genes and Omicron Infection
To examine the impact of hub genes on the symptoms manifested after SARSinfection in humans, we utilized GSE167930, which included healthy individuals, a tomatic infected individuals, symptomatic infected individuals, and recovering pa The analysis revealed no statistically significant difference in the expression of the 1 genes between asymptomatic infected individuals and healthy individuals. Howe notable statistically significant difference or a trend towards elevated expression w served for all 10 hub genes in symptomatic infected individuals compared to both h and asymptomatic infected individuals (although the difference was not statistical nificant in the latter case). Importantly, during the recovery period of COVID-19, 7 the 10 hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) exhibited a s cant decrease, reaching levels comparable to those of healthy individuals ( Figure 5 Furthermore, we conducted GO and KEGG enrichment analyses for the seve genes related to COVID-19 symptoms (Supplementary Table S4). The results ind that the most significant GO enrichment was observed for multiple type I interfer lated categories, including response to virus, response to type I interferon, cellu sponse to type I interferon, and type I interferon signaling pathway. The KEGG an demonstrated the involvement of various viral infectious diseases, including COV Based on the identified DEGs and the enrichment analyses of the hub genes, the highlighted the involvement of seven potential biomarkers in abnormal sig

Investigation of the Relationship between Hub Genes and Omicron Infection
To examine the impact of hub genes on the symptoms manifested after SARS-CoV-2 infection in humans, we utilized GSE167930, which included healthy individuals, asymptomatic infected individuals, symptomatic infected individuals, and recovering patients. The analysis revealed no statistically significant difference in the expression of the 10 hub genes between asymptomatic infected individuals and healthy individuals. However, a notable statistically significant difference or a trend towards elevated expression was observed for all 10 hub genes in symptomatic infected individuals compared to both healthy and asymptomatic infected individuals (although the difference was not statistically significant in the latter case). Importantly, during the recovery period of COVID-19, 7 out of the 10 hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) exhibited a significant decrease, reaching levels comparable to those of healthy individuals ( Figure 5A-J).
Furthermore, we conducted GO and KEGG enrichment analyses for the seven hub genes related to COVID-19 symptoms (Supplementary Table S4). The results indicated that the most significant GO enrichment was observed for multiple type I interferon-related categories, including response to virus, response to type I interferon, cellular response to type I interferon, and type I interferon signaling pathway. The KEGG analysis demonstrated the involvement of various viral infectious diseases, including COVID-19. Based on the identified DEGs and the enrichment analyses of the hub genes, the results highlighted the involvement of seven potential biomarkers in abnormal signaling pathways associated with COVID-19 symptom production and recovery, primarily related to type I interferon signaling pathways ( Figure 5K,L). pathways associated with COVID-19 symptom production and recovery, primarily related to type I interferon signaling pathways ( Figure 5K,L).

Validation of Hub Genes Expression by RT-qPCR
To validate the findings derived from the bioinformatics analysis, PBMC samples were collected from 20 COVID-19 patients infected with the Omicron variant and 20 healthy individuals as controls. The expression levels of the hub genes, namely IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15, were examined using RT-qPCR. The results revealed a significant upregulation of these genes in the COVID-19 group compared to the control group, which aligns with the patterns observed in the microarray analysis (Figure 6). This validation provides strong support for the reliability of the bioinformatics analysis and reinforces the evidence suggesting the dysregulation of these hub genes in COVID-19.

Validation of Hub Genes Expression by RT-qPCR
To validate the findings derived from the bioinformatics analysis, PBMC samples were collected from 20 COVID-19 patients infected with the Omicron variant and 20 healthy individuals as controls. The expression levels of the hub genes, namely IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15, were examined using RT-qPCR. The results revealed a significant upregulation of these genes in the COVID-19 group compared to the control group, which aligns with the patterns observed in the microarray analysis ( Figure 6). This validation provides strong support for the reliability of the bioinformatics analysis and reinforces the evidence suggesting the dysregulation of these hub genes in COVID-19.

Analysis of PMBC Immune Infiltration in Omicron Infection
Based on the GSE201530 dataset, "CIBERSORTx" (https://cibersortx.stanford. accessed on 20 May 2023) compared the different immune infiltration patterns of CO 19 patients and normal controls. The results showed that the proportion of monocy cells CD4 memory activated, and Mast cells resting was significantly increased in CO 19 patients with Omicron infection, while T cells CD4 memory resting was signific decreased ( Figure 8A). Cell types with an expression of 0 that were not present i sample were further excluded. The PBMC-associated immune cells were selected an results of correlation analysis between immune cells are shown in Figure 8B. . mRNA-miRNA and mRNA-TF interaction networks in COVID-19 symptom-related hub genes. mRNA and miRNAs co-expression network. The network depicts the interaction between the seven hub genes (IFI27, IFI44, IFI44L, ISG15, MX1, OAS3, and USP18) and their corresponding miRNAs. Various miRNAs, such as hsa-mir-146a-5p and hsa-mir-26b-5p, regulate the expression of these hub genes (A); mRNA and TF co-expression network. The network shows the interaction between the seven hub genes and transcription factors (TFs). TFs, including NR2C2 and ZNF143, are involved in regulating the expression of these hub genes (B).

Analysis of PMBC Immune Infiltration in Omicron Infection
Based on the GSE201530 dataset, "CIBERSORTx" (https://cibersortx.stanford.edu/, accessed on 20 May 2023) compared the different immune infiltration patterns of COVID-19 patients and normal controls. The results showed that the proportion of monocytes, T cells CD4 memory activated, and Mast cells resting was significantly increased in COVID-19 patients with Omicron infection, while T cells CD4 memory resting was significantly decreased ( Figure 8A). Cell types with an expression of 0 that were not present in the sample were further excluded. The PBMC-associated immune cells were selected and the results of correlation analysis between immune cells are shown in Figure 8B.

Target Drug Prediction
The DSigDB database was used to predict potential target drugs associated with seve target hub genes that may treat Omicron infection by modulating the hub genes. A total of 123 target drugs were finally predicted; the composite scores and corresponding target genes are listed in Supplementary Table S7. The top 10 predicted target drugs according to the composite scores are shown in Figure 9 The top 10 predicted targets according to the composite score are shown in Figure 9. Among them, acetohexamide is expected to be a potential drug for the treatment of Omicron infection.

Discussion
The emergence of novel coronaviruses and their potential global impact on public health have become a major concern in recent years [3]. With the ongoing challenges posed by the SARS-CoV-2 Omicron variant, it is crucial to identify potential biomarkers and explore associated mechanisms using bioinformatics approaches to enhance the diagnosis and treatment of this variant.
In this study, we analyzed the PBMC microarray dataset (GSE201530) from the GEO database to identify differentially expressed genes (DEGs) associated with SARS-CoV-2 Omicron variant infection. Through this analysis, we identified 10 hub genes through the construction of a protein-protein interaction (PPI) network. These findings were validated using an independent PBMC dataset of COVID-19 patients (GSE179627), which consistently demonstrated the expected expression patterns of these 10 genes and their diagnostic value. Additionally, we analyzed a dataset comprising healthy individuals, asymptomatic infected individuals, symptomatic infected individuals, and recovered infected individuals (GSE167930) to identify seven target hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) that showed correlations with symptom onset and recovery from COVID-19. Further GO and KEGG pathway analyses at different stages of genetic screening revealed significant enrichment in pathways related to virus response, defense response to virus, response to type I interferon, cellular response to type I interferon, and the type I interferon signaling pathway. Moreover, we analyzed the miRNAs

Discussion
The emergence of novel coronaviruses and their potential global impact on public health have become a major concern in recent years [3]. With the ongoing challenges posed by the SARS-CoV-2 Omicron variant, it is crucial to identify potential biomarkers and explore associated mechanisms using bioinformatics approaches to enhance the diagnosis and treatment of this variant.
In this study, we analyzed the PBMC microarray dataset (GSE201530) from the GEO database to identify differentially expressed genes (DEGs) associated with SARS-CoV-2 Omicron variant infection. Through this analysis, we identified 10 hub genes through the construction of a protein-protein interaction (PPI) network. These findings were validated using an independent PBMC dataset of COVID-19 patients (GSE179627), which consistently demonstrated the expected expression patterns of these 10 genes and their diagnostic value. Additionally, we analyzed a dataset comprising healthy individuals, asymptomatic infected individuals, symptomatic infected individuals, and recovered infected individuals (GSE167930) to identify seven target hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) that showed correlations with symptom onset and recovery from COVID-19. Further GO and KEGG pathway analyses at different stages of genetic screening revealed significant enrichment in pathways related to virus response, defense response to virus, response to type I interferon, cellular response to type I interferon, and the type I interferon signaling pathway. Moreover, we analyzed the miRNAs corresponding to these seven target hub genes and constructed interaction networks with transcription factors (TFs) using the miRNet tool.
In the analysis of the GSE201530 dataset with the "CIBERSORTx" tool, we observed a significant increase in the proportion of monocytes, activated memory CD4 T cells and resting mast cells in COVID-19 patients. Conversely, we observed a notable decrease in the proportion of resting memory T cells CD4. Furthermore, we identified a correlation between elevated monocyte levels and decreased proportions of resting memory T cells CD4. To support these findings, we reviewed blood counts recorded in hospital medical records of symptomatic COVID-19 patients with Omicron infection, which demonstrated that abnormally elevated monocyte ratios and higher monocyte counts were associated with an increased risk of developing symptomatic COVID-19 compared to normal subjects, aligning with the results obtained from immune infiltration analysis.
Moreover, we conducted RT-qPCR to validate the up-regulated expression levels of these seven genes in PBMCs of COVID-19 patients infected with the Omicron variant. The consistent up-regulation of these genes supports their potential crucial role in the progression of COVID-19. The expression trends of many key DEGs found in this study align with previously reported results, further validating the reliability of the database and our data analysis.
Among the seven target hub genes, MX1, OAS3, USP18, IFI27, and ISG15 are characterized as type I interferon-related genes [33]. MX1 acts as an effector protein of the IFN system and plays a crucial role in responding to SARS-CoV-2 infection, with its expression significantly increasing with viral load escalation [34]. Importantly, MX1 directly impacts the viral ribonucleoprotein complex, and its antiviral function relies on the essentiality of its gTPase activity [35]. The interferon-induced antiviral enzyme known as 2 -5 -oligoadenylate synthase (OAS) encompasses OAS1, OAS2, OAS3, and OASL [36]. OAS3 serves as a key player in antiviral action and signal transduction [37]. Additionally, IFI27 (also referred to as ISG12 or p27), an interferon α-inducible gene (and to a lesser extent, interferon γ), exhibits nuclear membrane localization and contributes to diverse biological processes [37]. Notably, a cohort study demonstrated that IFI27 expression was observed in the blood of COVID-19 patients and positively correlated with elevated viral load [38].
IFI44L, an IFN-inducible protein with similarities to IFN-I, is induced by various viruses [39]. As an IFN-I negative regulator, IFI44L mitigates antimicrobial inflammatory factors by negatively regulating the NF-κB pathway and inhibiting STAT1 activation, thereby inhibiting IFN-I and ISG production [40]. Acting as a feedback regulator of the IFN response, IFI44L potentially promotes viral replication by modulating the innate immune response following viral infection [41]. Interestingly, our study reveals a significant overlap between these seven target hub genes and the results obtained from bioinformatics analyses of diseases such as dengue fever [42]. Previous studies have demonstrated shared pathophysiological pathways between dengue fever and COVID-19, including fever, plasma leakage, low platelet count, and coagulation disorders [43]. Therefore, further discussions are warranted to explore potential therapeutic approaches to symptomatic COVID-19 by referencing strategies employed against dengue fever.
The initial defense against viral infection is the interferon I (IFN-I) response, which induces the activation of hundreds of interferon-stimulated genes (ISGs) through the JAK/STAT signaling pathway [44]. Severe lung inflammation leading to respiratory failure in SARS coronavirus type 2-infected patients is primarily attributed to cytokine dysregulation. Severe cases of COVID-19 exhibit impaired production of both IFN-I and interferon II (IFN-II) and downregulation of ISGs [45,46]. The hub genes identified in this study have the potential to counteract COVID-19 development by modulating interferon activity. To identify potential compounds, we conducted a screening process based on the DSigDB database. Among the ranked p-values, acetohexamide emerged as a promising candidate for COVID-19 patients with Omicron infection treatment. A study utilizing three molecular docking programs aimed to repurpose FDA-approved drugs targeting the functional structural domain of csBiP, specifically the BiP functional domain, as antivirals against COVID-19, and acetohexamide was among the selected ligands [47]. The potential therapeutic role of acetohexamide in the treatment of COVID-19, particularly in the context of Omicron infection, warrants further investigation and validation through in vitro and clinical studies. Such studies will help elucidate its mechanism of action and assess its safety and efficacy in combating the virus. This research direction represents a promising avenue for the development of targeted therapies to mitigate the impact of COVID-19, especially in the context of emerging variants like the Omicron variant.
Compared to previous studies on Omicron variants, this study brings innovation by specifically focusing on targets associated with symptom development and outcomes. The identification of hub genes related to the Type I interferon pathway highlights their critical role as key therapeutic targets. The validation of our results using patient samples in a clinical setting enhances the accuracy and scientific validity of our findings. However, this study has several limitations that should be acknowledged and taken into consideration. Firstly, there is still a lack of sufficient transcriptomic research related to Omicron variant infection, particularly concerning the differential expression of genes in individuals with different symptoms, leading to a scarcity of available datasets. Secondly, the diagnostic value of research findings such as hub genes and predicted drugs needs to be further verified by additional experimental exploration and clinical trials to establish their potential clinical relevance and utility, which is the direction of future research.
Despite these limitations, this study provides valuable insights into the molecular mechanisms underlying the pathogenesis of the SARS-CoV-2 Omicron variant and its association with the symptomatic presentation. The identified hub genes and enriched pathways offer potential targets for further investigation and the development of therapeutic interventions for Omicron-dominant COVID-19 cases. Further research in this area will be crucial for advancing our understanding of the virus and improving the management of COVID-19 patients.

Conclusions
In this study, our investigation successfully identified seven hub genes (IFI44, IFI44L, MX1, OAS3, USP18, IFI27, and ISG15) associated with antiviral activity and the type I interferon response as potential biomarkers for the diagnosis and treatment of COVID-19 in individuals infected with the Omicron variant. Through the construction of a comprehensive network of mRNA-miRNA and mRNA-TF interactions, we gained valuable insights into the regulatory mechanisms underlying Omicron infection. Our immune infiltration analysis and retrospective clinical data analysis provided compelling evidence of a correlation between elevated monocytes and the development of Omicron infection. We also identified ten agents as promising drug candidates that specifically target these hub genes for treating Omicron infection. These findings significantly advance our understanding of COVID-19 symptom development and recovery and highlight the potential of further investigating type I interferon-related pathways to identify therapeutic targets and biomarkers for COVID-19 patients, particularly those infected with the Omicron variant.